To develop an automated system that segments seven retinal sub-layers in OCT images of both normal eyes and eyes with diabetic macular edema (DME).

Methods

Heidelberg Spectralis OCT images were used. A total of 87 SD-OCT macular line scans were used with 22 of the scans containing significant disruptions of the retinal micro-structure due to cysts. For the algorithm each OCT image was subjected to automated speckle noise removal using both a Fourier-domain based error metric and a Wiener deconvolution algorithm. Next, an iterative multi-resolution filtering algorithm was used to detect the next most significant layer using high-pass filtering. Finally, the automated thickness profiles for each intraretinal layer were compared with manually marked ground-truth.